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| import torch | |
| DEVICE = "cuda" if torch.cuda.is_available() else "cpu" | |
| import numpy as np | |
| def subsequent_mask(size): | |
| attn_shape = (1, size, size) | |
| subsequent_mask = np.triu(np.ones(attn_shape), | |
| k=1).astype('uint8') | |
| output = torch.from_numpy(subsequent_mask) == 0 | |
| return output | |
| def make_std_mask(tgt, pad): | |
| tgt_mask=(tgt != pad).unsqueeze(-2) | |
| output=tgt_mask & subsequent_mask(tgt.size(-1)).type_as(tgt_mask.data) | |
| return output | |
| # define the Batch class | |
| class Batch: | |
| def __init__(self, src, trg=None, pad=0): | |
| src = torch.from_numpy(src).to(DEVICE).long() | |
| self.src = src | |
| self.src_mask = (src != pad).unsqueeze(-2) | |
| if trg is not None: | |
| trg = torch.from_numpy(trg).to(DEVICE).long() | |
| self.trg = trg[:, :-1] | |
| self.trg_y = trg[:, 1:] | |
| self.trg_mask = make_std_mask(self.trg, pad) | |
| self.ntokens = (self.trg_y != pad).data.sum() | |
| from torch import nn | |
| # An encoder-decoder transformer | |
| class Transformer(nn.Module): | |
| def __init__(self, encoder, decoder, | |
| src_embed, tgt_embed, generator): | |
| super().__init__() | |
| self.encoder = encoder | |
| self.decoder = decoder | |
| self.src_embed = src_embed | |
| self.tgt_embed = tgt_embed | |
| self.generator = generator | |
| def encode(self, src, src_mask): | |
| return self.encoder(self.src_embed(src), src_mask) | |
| def decode(self, memory, src_mask, tgt, tgt_mask): | |
| return self.decoder(self.tgt_embed(tgt), | |
| memory, src_mask, tgt_mask) | |
| def forward(self, src, tgt, src_mask, tgt_mask): | |
| memory = self.encode(src, src_mask) | |
| output = self.decode(memory, src_mask, tgt, tgt_mask) | |
| return output | |
| # Create an encoder | |
| from copy import deepcopy | |
| class Encoder(nn.Module): | |
| def __init__(self, layer, N): | |
| super().__init__() | |
| self.layers = nn.ModuleList( | |
| [deepcopy(layer) for i in range(N)]) | |
| self.norm = LayerNorm(layer.size) | |
| def forward(self, x, mask): | |
| for layer in self.layers: | |
| x = layer(x, mask) | |
| output = self.norm(x) | |
| return output | |
| class EncoderLayer(nn.Module): | |
| def __init__(self, size, self_attn, feed_forward, dropout): | |
| super().__init__() | |
| self.self_attn = self_attn | |
| self.feed_forward = feed_forward | |
| self.sublayer = nn.ModuleList([deepcopy( | |
| SublayerConnection(size, dropout)) for i in range(2)]) | |
| self.size = size | |
| def forward(self, x, mask): | |
| x = self.sublayer[0]( | |
| x, lambda x: self.self_attn(x, x, x, mask)) | |
| output = self.sublayer[1](x, self.feed_forward) | |
| return output | |
| class SublayerConnection(nn.Module): | |
| def __init__(self, size, dropout): | |
| super().__init__() | |
| self.norm = LayerNorm(size) | |
| self.dropout = nn.Dropout(dropout) | |
| def forward(self, x, sublayer): | |
| output = x + self.dropout(sublayer(self.norm(x))) | |
| return output | |
| class LayerNorm(nn.Module): | |
| def __init__(self, features, eps=1e-6): | |
| super().__init__() | |
| self.a_2 = nn.Parameter(torch.ones(features)) | |
| self.b_2 = nn.Parameter(torch.zeros(features)) | |
| self.eps = eps | |
| def forward(self, x): | |
| mean = x.mean(-1, keepdim=True) | |
| std = x.std(-1, keepdim=True) | |
| x_zscore = (x - mean) / torch.sqrt(std ** 2 + self.eps) | |
| output = self.a_2*x_zscore+self.b_2 | |
| return output | |
| # Create a decoder | |
| class Decoder(nn.Module): | |
| def __init__(self, layer, N): | |
| super().__init__() | |
| self.layers = nn.ModuleList( | |
| [deepcopy(layer) for i in range(N)]) | |
| self.norm = LayerNorm(layer.size) | |
| def forward(self, x, memory, src_mask, tgt_mask): | |
| for layer in self.layers: | |
| x = layer(x, memory, src_mask, tgt_mask) | |
| output = self.norm(x) | |
| return output | |
| class DecoderLayer(nn.Module): | |
| def __init__(self, size, self_attn, src_attn, | |
| feed_forward, dropout): | |
| super().__init__() | |
| self.size = size | |
| self.self_attn = self_attn | |
| self.src_attn = src_attn | |
| self.feed_forward = feed_forward | |
| self.sublayer = nn.ModuleList([deepcopy( | |
| SublayerConnection(size, dropout)) for i in range(3)]) | |
| def forward(self, x, memory, src_mask, tgt_mask): | |
| x = self.sublayer[0](x, lambda x: | |
| self.self_attn(x, x, x, tgt_mask)) | |
| x = self.sublayer[1](x, lambda x: | |
| self.src_attn(x, memory, memory, src_mask)) | |
| output = self.sublayer[2](x, self.feed_forward) | |
| return output | |
| # create the model | |
| def create_model(src_vocab, tgt_vocab, N, d_model, | |
| d_ff, h, dropout=0.1): | |
| attn=MultiHeadedAttention(h, d_model).to(DEVICE) | |
| ff=PositionwiseFeedForward(d_model, d_ff, dropout).to(DEVICE) | |
| pos=PositionalEncoding(d_model, dropout).to(DEVICE) | |
| model = Transformer( | |
| Encoder(EncoderLayer(d_model,deepcopy(attn),deepcopy(ff), | |
| dropout).to(DEVICE),N).to(DEVICE), | |
| Decoder(DecoderLayer(d_model,deepcopy(attn), | |
| deepcopy(attn),deepcopy(ff), dropout).to(DEVICE), | |
| N).to(DEVICE), | |
| nn.Sequential(Embeddings(d_model, src_vocab).to(DEVICE), | |
| deepcopy(pos)), | |
| nn.Sequential(Embeddings(d_model, tgt_vocab).to(DEVICE), | |
| deepcopy(pos)), | |
| Generator(d_model, tgt_vocab)).to(DEVICE) | |
| for p in model.parameters(): | |
| if p.dim() > 1: | |
| nn.init.xavier_uniform_(p) | |
| return model.to(DEVICE) | |
| import math | |
| class Embeddings(nn.Module): | |
| def __init__(self, d_model, vocab): | |
| super().__init__() | |
| self.lut = nn.Embedding(vocab, d_model) | |
| self.d_model = d_model | |
| def forward(self, x): | |
| out = self.lut(x) * math.sqrt(self.d_model) | |
| return out | |
| class PositionalEncoding(nn.Module): | |
| def __init__(self, d_model, dropout, max_len=5000): | |
| super().__init__() | |
| self.dropout = nn.Dropout(p=dropout) | |
| pe = torch.zeros(max_len, d_model, device=DEVICE) | |
| position = torch.arange(0., max_len, | |
| device=DEVICE).unsqueeze(1) | |
| div_term = torch.exp(torch.arange( | |
| 0., d_model, 2, device=DEVICE) | |
| * -(math.log(10000.0) / d_model)) | |
| pe_pos = torch.mul(position, div_term) | |
| pe[:, 0::2] = torch.sin(pe_pos) | |
| pe[:, 1::2] = torch.cos(pe_pos) | |
| pe = pe.unsqueeze(0) | |
| self.register_buffer('pe', pe) | |
| def forward(self, x): | |
| x = x + self.pe[:, :x.size(1)].requires_grad_(False) | |
| out = self.dropout(x) | |
| return out | |
| def attention(query, key, value, mask=None, dropout=None): | |
| d_k = query.size(-1) | |
| scores = torch.matmul(query, | |
| key.transpose(-2, -1)) / math.sqrt(d_k) | |
| if mask is not None: | |
| scores = scores.masked_fill(mask == 0, -1e9) | |
| p_attn = nn.functional.softmax(scores, dim=-1) | |
| if dropout is not None: | |
| p_attn = dropout(p_attn) | |
| return torch.matmul(p_attn, value), p_attn | |
| class MultiHeadedAttention(nn.Module): | |
| def __init__(self, h, d_model, dropout=0.1): | |
| super().__init__() | |
| assert d_model % h == 0 | |
| self.d_k = d_model // h | |
| self.h = h | |
| self.linears = nn.ModuleList([deepcopy( | |
| nn.Linear(d_model, d_model)) for i in range(4)]) | |
| self.attn = None | |
| self.dropout = nn.Dropout(p=dropout) | |
| def forward(self, query, key, value, mask=None): | |
| if mask is not None: | |
| mask = mask.unsqueeze(1) | |
| nbatches = query.size(0) | |
| query, key, value = [l(x).view(nbatches, -1, self.h, | |
| self.d_k).transpose(1, 2) | |
| for l, x in zip(self.linears, (query, key, value))] | |
| x, self.attn = attention( | |
| query, key, value, mask=mask, dropout=self.dropout) | |
| x = x.transpose(1, 2).contiguous().view( | |
| nbatches, -1, self.h * self.d_k) | |
| output = self.linears[-1](x) | |
| return output | |
| class Generator(nn.Module): | |
| def __init__(self, d_model, vocab): | |
| super().__init__() | |
| self.proj = nn.Linear(d_model, vocab) | |
| def forward(self, x): | |
| out = self.proj(x) | |
| probs = nn.functional.log_softmax(out, dim=-1) | |
| return probs | |
| class PositionwiseFeedForward(nn.Module): | |
| def __init__(self, d_model, d_ff, dropout=0.1): | |
| super().__init__() | |
| self.w_1 = nn.Linear(d_model, d_ff) | |
| self.w_2 = nn.Linear(d_ff, d_model) | |
| self.dropout = nn.Dropout(dropout) | |
| def forward(self, x): | |
| h1 = self.w_1(x) | |
| h2 = self.dropout(h1) | |
| return self.w_2(h2) | |
| class LabelSmoothing(nn.Module): | |
| def __init__(self, size, padding_idx, smoothing=0.1): | |
| super().__init__() | |
| self.criterion = nn.KLDivLoss(reduction='sum') | |
| self.padding_idx = padding_idx | |
| self.confidence = 1.0 - smoothing | |
| self.smoothing = smoothing | |
| self.size = size | |
| self.true_dist = None | |
| def forward(self, x, target): | |
| assert x.size(1) == self.size | |
| true_dist = x.data.clone() | |
| true_dist.fill_(self.smoothing / (self.size - 2)) | |
| true_dist.scatter_(1, | |
| target.data.unsqueeze(1), self.confidence) | |
| true_dist[:, self.padding_idx] = 0 | |
| mask = torch.nonzero(target.data == self.padding_idx) | |
| if mask.dim() > 0: | |
| true_dist.index_fill_(0, mask.squeeze(), 0.0) | |
| self.true_dist = true_dist | |
| output = self.criterion(x, true_dist.clone().detach()) | |
| return output | |
| class SimpleLossCompute: | |
| def __init__(self, generator, criterion, opt=None): | |
| self.generator = generator | |
| self.criterion = criterion | |
| self.opt = opt | |
| def __call__(self, x, y, norm): | |
| x = self.generator(x) | |
| loss = self.criterion(x.contiguous().view(-1, x.size(-1)), | |
| y.contiguous().view(-1)) / norm | |
| loss.backward() | |
| if self.opt is not None: | |
| self.opt.step() | |
| self.opt.optimizer.zero_grad() | |
| return loss.data.item() * norm.float() | |
| class NoamOpt: | |
| def __init__(self, model_size, factor, warmup, optimizer): | |
| self.optimizer = optimizer | |
| self._step = 0 | |
| self.warmup = warmup | |
| self.factor = factor | |
| self.model_size = model_size | |
| self._rate = 0 | |
| def step(self): | |
| self._step += 1 | |
| rate = self.rate() | |
| for p in self.optimizer.param_groups: | |
| p['lr'] = rate | |
| self._rate = rate | |
| self.optimizer.step() | |
| def rate(self, step=None): | |
| if step is None: | |
| step = self._step | |
| output = self.factor * (self.model_size ** (-0.5) * | |
| min(step ** (-0.5), step * self.warmup ** (-1.5))) | |
| return output | |